A handful of friends or a hundred acquaintances?

Michał Bojanowski

Autonomous University of Barcelona &
Kozminski University

2023-05-03

Personal network

Agenda

  1. Core and weak ties
  2. Methods of collecting data
  3. Preliminary results
  4. From personal to society-wide networks

Core and weak ties

Core ties

Core ties provide

  • Social support: material, economic, emotional
  • Feeling of being loved
  • Improve coping with daily challenges and getting ahead
  • Improve physical and mental health and well-being

Core networks

  • Relatively low degree (avg. 3 - 4)
  • Relationships are intimate and “strong”
  • Relationships usually parallel to kinship
  • Local in physical space (67% of core ties with 1 hr)
  • High social closure (local clustering)
  • Stable over time

Acquaintance ties

Acquaintance ties provide

Acquaintanship networks

  • Understudied!
  • Stereotype: these are “accidental ties”
    • Form randomly
    • Personal acquaintanship networks are alike across individuals
  • Transient ties seem stable (Hidd et al. 2023)

Questions

  • What is the distribution of acquaintanship network sizes?
  • How are acquaintanship network composed?
  • What is the effects ofacquaintanship network on opinions towards redistribution?

Methods

The approach

  • Estimate overall properties of acquaintanship network using NSUM
    • Degree distribution
    • Cohesiveness across categorical boundaries
  • Take samples of alters
    • Random samples based on names
    • Purposive samples based on social groups (positions and origins)

The BRIDGES survey

  • Country: Spain
  • \(N = 1500\)
  • Age: 18+
  • Mode: CAPI interviews
  • Interview time: ~ 30 minutes
  • Time: October - December 2021

Network Scale-Up Method (NSUM)

NSUM = Survey instrument + Statistical model

  • Estimating personal network sizes1
  • Estimating size(s) of hidden populations2

NSUM – survey instrument

BRIDGES survey

Now I will ask you about the people you know in Spain in general. I will ask about the people you know with certain characteristics. By knowing someone we understand that you know the first name of this person and you would recognize one another if you ran into them for example in the street, in a shop, or in another place. This includes both close relationships such as your partner, family, friends, neighbors, coworker or fellow students and less close relationships, such as for example people whom you have met in the associations to which you belong or who you know via other people.

These people do not have to live near you, you can also be in contact with them through social media or by phone. You may like them or not. Please do not include deceased persons, people under 18 years old, nor yourself.

How many people over the age of 18 do you know (by name and by sight) who have the following jobs, whether they are women or men?

NSUM positions

NSUM first names

NSUM estimator

Aggregated Relational Data (ARD)

  • \(y_{ik}\) – number of persons from subpopulation \(k\) “known” to \(i\)
  • \(N_k\) – known size of subpopulation \(k\) (e.g. from census statistics)

\[\hat{d}_i = \frac{\sum_k y_{ik}}{\sum_k N_k}\]

(Killworth, McCarty, et al. 1998; Killworth, Johnsen, et al. 1998)

NSUM – estimation and modeling

  • Types of parameters estimated
    • Estimated degree \(d_i\)
    • Overdispersion
    • Effects and biases
  • MLE (Killworth et al. 1990)
  • Bayesian hierarchical models
    • Zheng, Salganik, and Gelman (2006)
    • McCormick, Salganik, and Zheng (2010)
    • DiPrete et al. (2011)
    • Feehan et al. (2016)
    • Laga, Bao, and Niu (2021a)
    • Laga, Bao, and Niu (2021b) (a review)
    • Baum and Marsden (2023)

Illustrations

Degree distribution

Composition: occupations

Composition: gender

Effects on opinions on income redistribution

From personal to society-wide networks

Acquaintance ties – macro level

  • Social cohesion
    • Relations
    • Attachment/belonging
    • Orientation towards common good
  • Fractures across or along social and political divides
  • Weak ties are key to resilience
    • Strong ties build local cohesion but also fragment society
    • Weak ties are key to macrosocial integration (Blau 1974)
    • Building resilient societies (UN, 2021)

PATCHWORK project

  • Five European countries, representing different social cohesion regimes (Dragolov et al., 2016; Green and Janmaat, 2011)
    • Sweden, Hungary, Poland, Switzerland, The Netherlands
  • Categorical boundaries: Social class, ethnicity, religion, political orientation
  • Combines cross-national survey with qualitative analysis, agent-based modeling and simulation

ERC Advanced Grant (PI Miranda Lubbers)

The social fabric

  • Network spanning the whole society
  • Close friends and distant acquaintances
  • Ties along and across various social cleavages
  • Unfortunately unobservable by means of sociocentric methods

Partial network information

  • Probability sample of egocentric networks
  • Acquaintance relations: Network Scale-Up Method
  • Core relations: name generators and interpreters (Marsden 2011)

Computational approach to social cohesion

The COALESCE LAB

Stay in touch!

References

https://bit.ly/ars2023-keynote

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